Untitled
Ruben Arslan
2023-10-16
todo: manifeste rs aus rels berechnen
Load data
load("../gocd2/data/cleaned.rdata")
library(tidyverse)## Warning: package 'ggplot2' was built under R version 4.3.1## Warning: package 'lubridate' was built under R version 4.3.1## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(labelled)
saveRDS(s2_initial, file = "s2_initial.rds")
s2_initial %>% names()## [1] "session" "created"
## [3] "modified" "ended"
## [5] "expired" "bfi_open_1"
## [7] "narq_5" "narq_3"
## [9] "bfi_agree_2" "pvd_infectability_1"
## [11] "bfi_consc_2r" "soi_r_attitude_6r"
## [13] "asendorpf_shyness_4r" "bfi_neuro_2r"
## [15] "bfi_open_2" "bfi_extra_1"
## [17] "bfi_extra_3" "bfi_agree_3r"
## [19] "soi_r_attitude_4" "narq_15"
## [21] "bfi_consc_3" "soi_r_attitude_5"
## [23] "narq_2" "asendorpf_shyness_2"
## [25] "bfi_agree_1r" "bfi_consc_1"
## [27] "narq_11" "bfi_neuro_1"
## [29] "bfi_extra_2r" "bfi_neuro_3"
## [31] "narq_18" "bfi_open_3"
## [33] "asendorpf_shyness_5" "bfi_extra_4"
## [35] "narq_16" "bfi_agree_4"
## [37] "bfi_consc_9r" "bfi_neuro_4"
## [39] "bfi_open_4" "bfi_extra_5r"
## [41] "bfi_agree_5" "narq_1"
## [43] "bfi_consc_4r" "bfi_neuro_5r"
## [45] "bfi_open_5" "bfi_extra_6"
## [47] "narq_14" "bfi_agree_6r"
## [49] "bfi_consc_5" "asendorpf_shyness_3r"
## [51] "pvd_germ_aversion_2" "pvd_germ_aversion_3R"
## [53] "bfi_neuro_8" "pvd_infectability_3R"
## [55] "pvd_germ_aversion_1" "feel_safe_walking_dark"
## [57] "feel_safe_violent_crime" "feel_safe_sexual_assault"
## [59] "feel_safe_theft" "bfi_open_6"
## [61] "narq_17" "bfi_extra_7r"
## [63] "bfi_agree_7" "narq_4"
## [65] "asendorpf_shyness_1" "bfi_consc_6"
## [67] "bfi_neuro_6r" "bfi_open_7r"
## [69] "narq_13" "bfi_extra_8"
## [71] "bfi_agree_8r" "narq_6"
## [73] "bfi_consc_7" "bfi_neuro_7"
## [75] "narq_7" "bfi_open_8"
## [77] "narq_12" "bfi_open_9r"
## [79] "bfi_agree_9" "narq_8"
## [81] "bfi_consc_8r" "narq_9"
## [83] "narq_10" "bfi_open_10"
## [85] "pvd_infectability_2" "pvd_germ_aversion_4R"
## [87] "spms_partner_1" "spms_partner_2"
## [89] "spms_partner_3R" "satisfaction_sexual_intercourse"
## [91] "satisfaction_single_life" "investment_potential_partner"
## [93] "timeperiod_potential_partner" "characteristics_potential_partner"
## [95] "quantity_potential_partner" "sexual_partner"
## [97] "fling_frequency" "fling_frequency_2"
## [99] "relationship_importance" "relationship_importance_partner"
## [101] "partner_attractiveness_longterm" "partner_attractiveness_shortterm"
## [103] "partner_attractiveness_face" "partner_attractiveness_body"
## [105] "attractiveness_warmth" "partner_attractiveness_trust"
## [107] "net_income_partner" "partner_sexiness"
## [109] "partner_strength" "partner_feel_safe"
## [111] "spms_self_1" "spms_self_2"
## [113] "spms_self_3R" "meet_potential_partner"
## [115] "partner_height" "meet_potential_partner_other"
## [117] "partner_weight" "soi_r_behavior_1"
## [119] "soi_r_behavior_2" "soi_r_behavior_3"
## [121] "soi_r_desire_9" "soi_r_desire_7"
## [123] "soi_r_desire_8" "relationship_problems"
## [125] "relationship_satisfaction_overall" "relationship_conflict"
## [127] "relationship_satisfaction_2" "relationship_satisfaction_3"
## [129] "alternatives_1" "alternatives_2"
## [131] "alternatives_3" "alternatives_4"
## [133] "alternatives_5" "alternatives_6"
## [135] "investment_1" "investment_2"
## [137] "investment_3" "commitment_1"
## [139] "commitment_2" "commitment_3"
## [141] "communal_strength_1" "communal_strength_2R"
## [143] "communal_strength_3" "communal_strength_4"
## [145] "sexual_communal_strength_1" "sexual_communal_strength_2"
## [147] "sexual_communal_strength_3" "ecr_avo_1R"
## [149] "ecr_anx_1" "ecr_avo_2"
## [151] "ecr_anx_2" "ecr_anx_3"
## [153] "ecr_avo_3R" "ecr_avo_4"
## [155] "ecr_anx_4R" "ecr_avo_5R"
## [157] "ecr_anx_5" "ecr_avo_6"
## [159] "ecr_anx_6" "free_not_covered"
## [161] "narq" "bfi_open"
## [163] "bfi_extra" "bfi_agree"
## [165] "soi_r_attitude" "bfi_consc"
## [167] "asendorpf_shyness" "bfi_neuro"
## [169] "pvd_germ_aversion" "pvd_infectability"
## [171] "spms_partner" "spms_self"
## [173] "relationship_satisfaction" "alternatives"
## [175] "investment" "commitment"
## [177] "communal_strength" "sexual_communal_strength"
## [179] "ecr_avo" "ecr_anx"
## [181] "short" "soi_r_desire"
## [183] "soi_r_behavior_1_discrete" "soi_r_behavior_2_discrete"
## [185] "soi_r_behavior_3_discrete" "soi_r_behavior"
## [187] "soi_r" "spms_rel"
## [189] "partner_attractiveness_sexual" "relationship_conflict_R"
## [191] "relationship_problems_R"scales <- s2_initial %>% select(narq:ecr_anx) %>% map(attributes)
likert_items <- s2_initial %>% select(bfi_open_1, narq_5, narq_3, bfi_agree_2, pvd_infectability_1, bfi_consc_2r, soi_r_attitude_6r, asendorpf_shyness_4r, bfi_neuro_2r, bfi_open_2, bfi_extra_1, bfi_extra_3, bfi_agree_3r, soi_r_attitude_4, narq_15, bfi_consc_3, soi_r_attitude_5, narq_2, asendorpf_shyness_2, bfi_agree_1r, bfi_consc_1, narq_11, bfi_neuro_1, bfi_extra_2r, bfi_neuro_3, narq_18, bfi_open_3, asendorpf_shyness_5, bfi_extra_4, narq_16, bfi_agree_4, bfi_consc_9r, bfi_neuro_4, bfi_open_4, bfi_extra_5r, bfi_agree_5, narq_1, bfi_consc_4r, bfi_neuro_5r, bfi_open_5, bfi_extra_6, narq_14, bfi_agree_6r, bfi_consc_5, asendorpf_shyness_3r, pvd_germ_aversion_2, pvd_germ_aversion_3R, bfi_neuro_8, pvd_infectability_3R, pvd_germ_aversion_1, feel_safe_walking_dark, feel_safe_violent_crime, feel_safe_sexual_assault, feel_safe_theft, bfi_open_6, narq_17, bfi_extra_7r, bfi_agree_7, narq_4, asendorpf_shyness_1, bfi_consc_6, bfi_neuro_6r, bfi_open_7r, narq_13, bfi_extra_8, bfi_agree_8r, narq_6, bfi_consc_7, bfi_neuro_7, narq_7, bfi_open_8, narq_12, bfi_open_9r, bfi_agree_9, narq_8, bfi_consc_8r, narq_9, narq_10, bfi_open_10, pvd_infectability_2, pvd_germ_aversion_4R, spms_partner_1, spms_partner_2, spms_partner_3R, satisfaction_sexual_intercourse, satisfaction_single_life, investment_potential_partner, quantity_potential_partner, relationship_importance, relationship_importance_partner, partner_attractiveness_longterm, partner_attractiveness_shortterm, partner_attractiveness_face, partner_attractiveness_body, attractiveness_warmth, partner_attractiveness_trust, partner_sexiness, partner_strength, partner_feel_safe, spms_self_1, spms_self_2, spms_self_3R, soi_r_desire_9, soi_r_desire_7, soi_r_desire_8, relationship_problems, relationship_satisfaction_overall, relationship_conflict, relationship_satisfaction_2, relationship_satisfaction_3, alternatives_1, alternatives_2, alternatives_3, alternatives_4, alternatives_5, alternatives_6, investment_1, investment_2, investment_3, commitment_1, commitment_2, commitment_3, communal_strength_1, communal_strength_2R, communal_strength_3, communal_strength_4, sexual_communal_strength_1, sexual_communal_strength_2, sexual_communal_strength_3, ecr_avo_1R, ecr_anx_1, ecr_avo_2, ecr_anx_2, ecr_anx_3, ecr_avo_3R, ecr_avo_4, ecr_anx_4R, ecr_avo_5R, ecr_anx_5, ecr_avo_6, ecr_anx_6)
item_table <- rio::import("https://docs.google.com/spreadsheets/d/1apYY4LZO05plk8jr-EVQUQfLWW2fgBfMrX2ntmPEMdc/edit#gid=483254913", format = "xlsx")
var_label(likert_items$soi_r_desire_8) <- "Wie oft empfinden Sie sexuelle Erregung im Kontakt mit Personen, mit denen Sie zur Zeit keine feste Beziehung führen?"
var_label(likert_items$commitment_2) <- "Ich orientiere mich an einer langfristigen Zukunft unserer Partnerschaft (z.B. stelle mir unser Zusammensein in einigen Jahren vor, mache Pläne für die Zukunft)."
var_label(likert_items$narq_1) <- "Ich bin großartig."
library(codebook)##
## Attaching package: 'codebook'
##
## The following object is masked from 'package:labelled':
##
## to_factorlikert_items <- likert_items %>%
mutate_at(vars(matches("\\d[rR]$")), reverse_labelled_values)
correlations <- likert_items %>% cor(use = 'p') %>%
as.data.frame() %>%
as_tibble(rownames = "item") %>%
pivot_longer(-item) %>%
rename(item_1 = item, item_2 = name, correlation = value) %>%
filter(correlation != 1, !is.na(correlation))
correlations %>%
rio::export("item_correlations.csv")
correlations %>% select(-correlation) %>%
rio::export("possible_item_pairs.csv")
likert_items_with_labels <-
likert_items %>% var_label() %>%
enframe("item_name", "label") %>%
unnest(label) %>%
left_join(item_table %>% select(label, label_en, showif), by = c("label" = "label"))
likert_items_with_labels %>%
rio::export("gocd2_item_labels.csv")Test
llm0710 <- rio::import("ruben-test-item-similarity-20230710-164559.csv")
llm1018 <- rio::import("ruben-test-item-similarity-20231018-122504.csv")
llm0809 <- rio::import("ruben-test-ItemSimilarityTraining-20230809-trial106.csv")
names(llm0710) <- c("row", "item_1_label", "item_2_label", "similarity_0710")
names(llm1018) <- c("row", "item_1_label", "item_2_label", "similarity_1018")
names(llm0809) <- c("row", "item_1_label", "item_2_label", "similarity_0809")
llm0710 <- llm0710 %>%
mutate(
item_1_label = str_replace_all(item_1_label, '""', '"'),
item_2_label = str_replace_all(item_2_label, '""', '"')
)
llm1018 <- llm1018 %>%
mutate(
item_1_label = str_replace_all(item_1_label, '""', '"'),
item_2_label = str_replace_all(item_2_label, '""', '"')
)
llm0809 <- llm0809 %>%
mutate(
item_1_label = str_replace_all(item_1_label, '""', '"'),
item_2_label = str_replace_all(item_2_label, '""', '"')
)
# correlations %>%
# full_join(likert_items_with_labels %>%
# select(item_name, item_1_label = label_en),
# by = c("item_1" = "item_name")) %>%
# anti_join(llm0710, by = c("item_1_label")) %>%
# select(item_1_label) %>%
# View()
correlations_llm <- correlations %>%
full_join(likert_items_with_labels %>%
select(item_name, item_1_label = label_en),
by = c("item_1" = "item_name")) %>%
full_join(likert_items_with_labels %>%
select(item_name, item_2_label = label_en),
by = c("item_2" = "item_name")) %>%
full_join(llm0710, by = c("item_1_label", "item_2_label")) %>%
full_join(llm1018, by = c("item_1_label", "item_2_label")) %>%
full_join(llm0809, by = c("item_1_label", "item_2_label"))
correlations_llm %>% drop_na() %>% nrow()## [1] 9717correlations_llm %>%
select(correlation, similarity_0710, similarity_1018, similarity_0809) %>%
cor(use = 'p')## correlation similarity_0710 similarity_1018 similarity_0809
## correlation 1.0000000 0.5714303 0.5869598 0.6014052
## similarity_0710 0.5714303 1.0000000 0.8182191 0.8719794
## similarity_1018 0.5869598 0.8182191 1.0000000 0.8580867
## similarity_0809 0.6014052 0.8719794 0.8580867 1.0000000correlations_llm %>%
filter(!str_detect(item_1, "^bfi_"), !str_detect(item_2, "^bfi_")) %>%
filter(!str_detect(item_1, "^narq"), !str_detect(item_2, "^narq_")) %>%
filter(!str_detect(item_1, "^asendorpf"), !str_detect(item_2, "^asendorpf")) %>%
select(correlation, similarity_0710, similarity_1018, similarity_0809) %>%
cor(use = 'p')## correlation similarity_0710 similarity_1018 similarity_0809
## correlation 1.0000000 0.5192127 0.5035130 0.5421965
## similarity_0710 0.5192127 1.0000000 0.7842760 0.8324001
## similarity_1018 0.5035130 0.7842760 1.0000000 0.8599076
## similarity_0809 0.5421965 0.8324001 0.8599076 1.0000000correlations_llm %>%
select(correlation, similarity_0710, similarity_1018, similarity_0809) %>%
mutate_all(abs) %>%
cor(use = 'p')## correlation similarity_0710 similarity_1018 similarity_0809
## correlation 1.0000000 0.4765112 0.4745521 0.4885470
## similarity_0710 0.4765112 1.0000000 0.7061711 0.7973930
## similarity_1018 0.4745521 0.7061711 1.0000000 0.7547388
## similarity_0809 0.4885470 0.7973930 0.7547388 1.0000000ggplot(correlations_llm, aes(correlation, similarity_0710)) +
geom_point(alpha = 0.05)## Warning: Removed 10616 rows containing missing values (`geom_point()`).
ggplot(correlations_llm, aes(correlation, similarity_1018)) +
geom_point(alpha = 0.2)## Warning: Removed 10616 rows containing missing values (`geom_point()`).
ggplot(correlations_llm, aes(correlation, similarity_0809)) +
geom_point(alpha = 0.2)## Warning: Removed 10616 rows containing missing values (`geom_point()`).
library(plotly) ## Warning: package 'plotly' was built under R version 4.3.1##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutcorrelations_llm %>%
# filter(str_detect(item_1, "bfi_agree"), str_detect(item_2, "bfi_agree")) %>%
mutate(correlation = round(correlation, 2)) %>%
mutate(similarity_0710 = round(similarity_0710, 2)) %>%
mutate(items = str_c(item_1_label, "\n", item_2_label)) %>%
ggplot(., aes(correlation, similarity_0710, label = items)) +
geom_point() -> p
ggplotly(p)correlations_llm %>%
mutate(correlation = round(correlation, 2)) %>%
mutate(similarity_1018 = round(similarity_1018, 2)) %>%
mutate(items = str_c(item_1_label, "\n", item_2_label)) %>%
ggplot(., aes(correlation, similarity_1018, label = items)) +
geom_point() -> p
ggplotly(p)correlations_llm %>%
mutate(correlation = round(correlation, 2)) %>%
mutate(similarity_0809 = round(similarity_0809, 2)) %>%
mutate(items = str_c(item_1_label, "\n", item_2_label)) %>%
ggplot(., aes(correlation, similarity_0809, label = items)) +
geom_point() -> p
ggplotly(p)Scale level
library(lavaan)## This is lavaan 0.6-16
## lavaan is FREE software! Please report any bugs.library(corrr)
tibble <- likert_items %>% cor(use = 'p') %>% as_cordf() %>%
shave() %>%
stretch() %>%
drop_na()
empirical_cors <- tibble %>% as.data.frame() |>
igraph::graph_from_data_frame(directed = FALSE) |>
igraph::as_adjacency_matrix(attr = "r", sparse = FALSE)
diag(empirical_cors) <- 1
# empirical_cors2 <- likert_items %>% cor(use = 'p')
# empirical_cors %>% as_cordf() %>% stretch() %>% left_join(empirical_cors2 %>% as_cordf() %>% stretch(), by = c("x", "y")) %>% filter(round(r.x,2) != round(r.y, 2))
empirical_cors[str_detect(rownames(empirical_cors), "\\d[rR]$"), ] <-
empirical_cors[str_detect(rownames(empirical_cors), "\\d[rR]$"), ] * -1
empirical_cors[, str_detect(colnames(empirical_cors), "\\d[rR]$")] <-
empirical_cors[, str_detect(colnames(empirical_cors), "\\d[rR]$")] * -1
empirical_cors["bfi_agree_3r", "bfi_agree_1r"]## [1] 0.3932222empirical_cors["bfi_agree_2", "bfi_agree_1r"]## [1] 0.174473#> This is lavaan 0.6-14
#> lavaan is FREE software! Please report any bugs.
lcor <- cfa("bfi_neuro =~ bfi_neuro_2r + bfi_neuro_1 + bfi_neuro_3 + bfi_neuro_4 + bfi_neuro_5r + bfi_neuro_8 + bfi_neuro_6r + bfi_neuro_7
spms_self =~ spms_self_1 + spms_self_2 + spms_self_3R",
sample.cov = empirical_cors, sample.nobs = 1000)
summary(lcor, standardized = T, ci=T)## lavaan 0.6.16 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 23
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 366.819
## Degrees of freedom 43
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## bfi_neuro =~
## bfi_neuro_2r 1.000 1.000 1.000
## bfi_neuro_1 0.729 0.047 15.467 0.000 0.637 0.822
## bfi_neuro_3 0.950 0.047 20.041 0.000 0.857 1.043
## bfi_neuro_4 0.774 0.047 16.413 0.000 0.682 0.867
## bfi_neuro_5r 0.988 0.048 20.806 0.000 0.895 1.081
## bfi_neuro_8 0.789 0.047 16.708 0.000 0.696 0.881
## bfi_neuro_6r 0.998 0.048 20.995 0.000 0.905 1.091
## bfi_neuro_7 0.847 0.047 17.918 0.000 0.754 0.939
## spms_self =~
## spms_self_1 1.000 1.000 1.000
## spms_self_2 1.069 0.040 26.959 0.000 0.991 1.147
## spms_self_3R 0.912 0.037 24.703 0.000 0.839 0.984
## Std.lv Std.all
##
## 0.725 0.725
## 0.529 0.529
## 0.689 0.689
## 0.562 0.562
## 0.717 0.717
## 0.572 0.572
## 0.724 0.724
## 0.614 0.614
##
## 0.821 0.821
## 0.878 0.878
## 0.748 0.749
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## bfi_neuro ~~
## spms_self -0.109 0.022 -4.860 0.000 -0.153 -0.065
## Std.lv Std.all
##
## -0.183 -0.183
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .bfi_neuro_2r 0.473 0.026 18.260 0.000 0.423 0.524
## .bfi_neuro_1 0.719 0.034 20.938 0.000 0.652 0.787
## .bfi_neuro_3 0.524 0.028 19.026 0.000 0.470 0.578
## .bfi_neuro_4 0.684 0.033 20.671 0.000 0.619 0.749
## .bfi_neuro_5r 0.485 0.026 18.456 0.000 0.434 0.537
## .bfi_neuro_8 0.672 0.033 20.579 0.000 0.608 0.736
## .bfi_neuro_6r 0.475 0.026 18.295 0.000 0.425 0.526
## .bfi_neuro_7 0.622 0.031 20.137 0.000 0.562 0.683
## .spms_self_1 0.325 0.023 13.890 0.000 0.279 0.371
## .spms_self_2 0.229 0.023 9.825 0.000 0.183 0.274
## .spms_self_3R 0.439 0.025 17.635 0.000 0.390 0.488
## bfi_neuro 0.526 0.042 12.488 0.000 0.443 0.608
## spms_self 0.674 0.046 14.634 0.000 0.584 0.764
## Std.lv Std.all
## 0.473 0.474
## 0.719 0.720
## 0.524 0.525
## 0.684 0.684
## 0.485 0.486
## 0.672 0.673
## 0.475 0.476
## 0.622 0.623
## 0.325 0.325
## 0.229 0.229
## 0.439 0.439
## 1.000 1.000
## 1.000 1.000semTools::compRelSEM(lcor)## bfi_neuro spms_self
## 0.844 0.858tibble <- llm1018 %>%
full_join(likert_items_with_labels %>%
select(item_name_1 = item_name, item_1_label = label_en)) %>%
full_join(likert_items_with_labels %>%
select(item_name_2 = item_name, item_2_label = label_en)) %>%
select(x = item_name_1, y = item_name_2, r = similarity_1018) %>%
drop_na()## Joining with `by = join_by(item_1_label)`## Joining with `by = join_by(item_2_label)`cors <- tibble %>% as.data.frame() |>
igraph::graph_from_data_frame(directed = FALSE) |>
igraph::as_adjacency_matrix(attr = "r", sparse = FALSE)
diag(cors) <- 1
# library(corrr)
# cors <- retract(tibble) %>% as_cordf() %>% shave(upper = F) %>%
# as_matrix()
# cors[lower.tri(cors)] <- t(cors)[lower.tri(cors)]
# diag(cors) <- 1
cors[str_detect(rownames(cors), "\\d[rR]$"), ] <-
cors[str_detect(rownames(cors), "\\d[rR]$"), ] * -1
cors[, str_detect(colnames(cors), "\\d[rR]$")] <-
cors[, str_detect(colnames(cors), "\\d[rR]$")] * -1
cors["bfi_agree_3r", "bfi_agree_1r"]## [1] 0.7062705cors["bfi_agree_2", "bfi_agree_3r"]## [1] 0.02743899cors["bfi_agree_2", "bfi_agree_1r"]## [1] 0.03940754library(semTools)##
## ###############################################################################
## This is semTools 0.5-6
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
##
## Attaching package: 'semTools'
##
## The following object is masked from 'package:readr':
##
## clipboardlcor <- cfa("bfi_neuro =~ bfi_neuro_2r + bfi_neuro_1 + bfi_neuro_3 + bfi_neuro_4 + + bfi_neuro_8 + bfi_neuro_6r + bfi_neuro_7
spms_self =~ spms_self_1 + spms_self_2 + spms_self_3R",
sample.cov = cors, sample.nobs = 1000)
semTools::compRelSEM(lcor)## bfi_neuro spms_self
## 0.734 0.679summary(lcor, standardized = T, ci=T)## lavaan 0.6.16 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 659.252
## Degrees of freedom 34
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## bfi_neuro =~
## bfi_neuro_2r 1.000 1.000 1.000
## bfi_neuro_1 3.887 1.049 3.704 0.000 1.830 5.943
## bfi_neuro_3 6.322 1.673 3.778 0.000 3.043 9.602
## bfi_neuro_4 5.136 1.368 3.756 0.000 2.456 7.816
## bfi_neuro_8 5.852 1.552 3.771 0.000 2.811 8.893
## bfi_neuro_6r 1.931 0.570 3.388 0.001 0.814 3.048
## bfi_neuro_7 5.938 1.574 3.773 0.000 2.853 9.022
## spms_self =~
## spms_self_1 1.000 1.000 1.000
## spms_self_2 0.720 0.112 6.422 0.000 0.500 0.940
## spms_self_3R 0.234 0.047 4.961 0.000 0.142 0.327
## Std.lv Std.all
##
## 0.129 0.129
## 0.501 0.501
## 0.814 0.815
## 0.661 0.662
## 0.754 0.754
## 0.249 0.249
## 0.765 0.765
##
## 0.943 0.944
## 0.679 0.680
## 0.221 0.221
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## bfi_neuro ~~
## spms_self 0.009 0.005 1.771 0.077 -0.001 0.018
## Std.lv Std.all
##
## 0.072 0.072
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .bfi_neuro_2r 0.982 0.044 22.297 0.000 0.896 1.069
## .bfi_neuro_1 0.748 0.036 21.079 0.000 0.679 0.818
## .bfi_neuro_3 0.336 0.023 14.539 0.000 0.291 0.381
## .bfi_neuro_4 0.561 0.029 19.331 0.000 0.505 0.618
## .bfi_neuro_8 0.431 0.025 17.137 0.000 0.382 0.480
## .bfi_neuro_6r 0.937 0.042 22.110 0.000 0.854 1.020
## .bfi_neuro_7 0.414 0.025 16.749 0.000 0.366 0.463
## .spms_self_1 0.110 0.135 0.811 0.418 -0.155 0.375
## .spms_self_2 0.538 0.074 7.251 0.000 0.392 0.683
## .spms_self_3R 0.950 0.043 22.017 0.000 0.866 1.035
## bfi_neuro 0.017 0.009 1.892 0.058 -0.001 0.034
## spms_self 0.889 0.142 6.251 0.000 0.611 1.168
## Std.lv Std.all
## 0.982 0.983
## 0.748 0.749
## 0.336 0.336
## 0.561 0.562
## 0.431 0.431
## 0.937 0.938
## 0.414 0.415
## 0.110 0.110
## 0.538 0.538
## 0.950 0.951
## 1.000 1.000
## 1.000 1.000scales$relationship_satisfaction <- NULL
for(i in seq_along(scales)) {
scale <- names(scales)[i]
items <- scales[[i]]$scale_item_names
scales[[i]]$lvn <- paste(scale, " =~ ", paste(items, collapse = " + "))
}
model <- scales %>% map(~ .$lvn) %>% paste(collapse = "\n")
lcor_real <- cfa(model,
sample.cov = empirical_cors, sample.nobs = 1000)
rels_real <- semTools::compRelSEM(lcor_real)
summary(lcor_real)## lavaan 0.6.16 ended normally after 271 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 399
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 19232.807
## Degrees of freedom 6156
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## narq =~
## narq_5 1.000
## narq_3 1.260 0.109 11.531 0.000
## narq_15 1.235 0.108 11.415 0.000
## narq_2 1.126 0.104 10.866 0.000
## narq_11 0.534 0.084 6.361 0.000
## narq_18 0.767 0.090 8.480 0.000
## narq_16 1.400 0.116 12.117 0.000
## narq_1 1.046 0.100 10.415 0.000
## narq_14 0.539 0.084 6.413 0.000
## narq_17 1.018 0.099 10.252 0.000
## narq_4 1.169 0.105 11.089 0.000
## narq_13 0.877 0.094 9.317 0.000
## narq_6 1.080 0.102 10.611 0.000
## narq_7 1.192 0.106 11.209 0.000
## narq_12 1.131 0.104 10.894 0.000
## narq_8 1.396 0.115 12.099 0.000
## narq_9 1.105 0.103 10.750 0.000
## narq_10 1.171 0.106 11.103 0.000
## bfi_open =~
## bfi_open_1 1.000
## bfi_open_2 0.668 0.049 13.654 0.000
## bfi_open_3 0.566 0.049 11.621 0.000
## bfi_open_4 0.898 0.050 18.112 0.000
## bfi_open_5 1.062 0.050 21.056 0.000
## bfi_open_6 0.781 0.049 15.874 0.000
## bfi_open_7r 0.419 0.049 8.635 0.000
## bfi_open_8 0.891 0.050 17.989 0.000
## bfi_open_9r 0.804 0.049 16.312 0.000
## bfi_open_10 0.660 0.049 13.494 0.000
## bfi_extra =~
## bfi_extra_1 1.000
## bfi_extra_3 0.708 0.043 16.290 0.000
## bfi_extra_2r 1.078 0.042 25.460 0.000
## bfi_extra_4 0.782 0.043 18.054 0.000
## bfi_extra_5r 1.045 0.042 24.623 0.000
## bfi_extra_6 0.635 0.044 14.541 0.000
## bfi_extra_7r 0.984 0.043 23.068 0.000
## bfi_extra_8 1.133 0.042 26.883 0.000
## bfi_agree =~
## bfi_agree_2 1.000
## bfi_agree_3r 1.043 0.109 9.580 0.000
## bfi_agree_1r 1.285 0.120 10.682 0.000
## bfi_agree_4 1.041 0.109 9.568 0.000
## bfi_agree_5 1.053 0.109 9.630 0.000
## bfi_agree_6r 1.629 0.139 11.760 0.000
## bfi_agree_7 1.080 0.111 9.771 0.000
## bfi_agree_8r 1.800 0.149 12.119 0.000
## bfi_agree_9 0.962 0.105 9.126 0.000
## soi_r_attitude =~
## soi_r_atttd_6r 1.000
## soi_r_attitd_4 0.962 0.038 25.652 0.000
## soi_r_attitd_5 1.000 0.038 26.467 0.000
## bfi_consc =~
## bfi_consc_2r 1.000
## bfi_consc_3 1.483 0.128 11.628 0.000
## bfi_consc_1 1.555 0.131 11.843 0.000
## bfi_consc_9r 1.260 0.116 10.819 0.000
## bfi_consc_4r 1.407 0.124 11.378 0.000
## bfi_consc_5 1.465 0.127 11.570 0.000
## bfi_consc_6 1.428 0.125 11.451 0.000
## bfi_consc_7 1.367 0.122 11.236 0.000
## bfi_consc_8r 1.334 0.120 11.115 0.000
## asendorpf_shyness =~
## asndrpf_shyn_4 1.000
## asndrpf_shyn_2 0.937 0.037 25.007 0.000
## asndrpf_shyn_5 0.715 0.039 18.192 0.000
## asndrpf_shyn_3 1.032 0.037 28.270 0.000
## asndrpf_shyn_1 1.033 0.037 28.299 0.000
## bfi_neuro =~
## bfi_neuro_2r 1.000
## bfi_neuro_1 0.825 0.051 16.310 0.000
## bfi_neuro_3 1.027 0.052 19.931 0.000
## bfi_neuro_4 0.834 0.051 16.484 0.000
## bfi_neuro_5r 0.996 0.051 19.402 0.000
## bfi_neuro_8 0.863 0.051 17.005 0.000
## bfi_neuro_6r 0.997 0.051 19.408 0.000
## bfi_neuro_7 0.922 0.051 18.091 0.000
## pvd_germ_aversion =~
## pvd_grm_vrsn_2 1.000
## pvd_grm_vrs_3R 0.944 0.088 10.775 0.000
## pvd_grm_vrsn_1 0.999 0.091 11.030 0.000
## pvd_grm_vrs_4R 0.656 0.077 8.523 0.000
## pvd_infectability =~
## pvd_nfctblty_1 1.000
## pvd_nfctblt_3R 0.889 0.037 23.776 0.000
## pvd_nfctblty_2 0.894 0.037 23.879 0.000
## spms_partner =~
## spms_partner_1 1.000
## spms_partner_2 1.088 0.047 23.328 0.000
## spms_partnr_3R 0.875 0.042 20.994 0.000
## spms_self =~
## spms_self_1 1.000
## spms_self_2 1.057 0.036 29.071 0.000
## spms_self_3R 0.909 0.036 25.304 0.000
## alternatives =~
## alternatives_1 1.000
## alternatives_2 0.881 0.048 18.273 0.000
## alternatives_3 0.725 0.048 15.138 0.000
## alternatives_4 0.776 0.048 16.180 0.000
## alternatives_5 0.856 0.048 17.772 0.000
## alternatives_6 0.631 0.048 13.215 0.000
## investment =~
## investment_1 1.000
## investment_2 0.855 0.066 12.869 0.000
## investment_3 0.529 0.057 9.261 0.000
## commitment =~
## commitment_1 1.000
## commitment_2 0.943 0.032 29.920 0.000
## commitment_3 0.918 0.032 28.847 0.000
## communal_strength =~
## cmmnl_strngt_1 1.000
## cmmnl_strng_2R 0.749 0.046 16.168 0.000
## cmmnl_strngt_3 0.859 0.047 18.440 0.000
## cmmnl_strngt_4 0.651 0.046 14.078 0.000
## sexual_communal_strength =~
## sxl_cmmnl_st_1 1.000
## sxl_cmmnl_st_2 1.131 0.053 21.397 0.000
## sxl_cmmnl_st_3 0.520 0.044 11.726 0.000
## ecr_avo =~
## ecr_avo_1R 1.000
## ecr_avo_2 0.470 0.043 10.804 0.000
## ecr_avo_3R 1.023 0.042 24.428 0.000
## ecr_avo_4 0.624 0.043 14.493 0.000
## ecr_avo_5R 1.029 0.042 24.583 0.000
## ecr_avo_6 0.491 0.043 11.297 0.000
## ecr_anx =~
## ecr_anx_1 1.000
## ecr_anx_2 5.040 1.051 4.797 0.000
## ecr_anx_3 5.557 1.158 4.798 0.000
## ecr_anx_4R 1.867 0.435 4.294 0.000
## ecr_anx_5 1.201 0.322 3.725 0.000
## ecr_anx_6 3.607 0.765 4.712 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## narq ~~
## bfi_open 0.096 0.014 6.822 0.000
## bfi_extra 0.093 0.014 6.680 0.000
## bfi_agree -0.065 0.010 -6.575 0.000
## soi_r_attitude 0.031 0.014 2.297 0.022
## bfi_consc 0.002 0.007 0.236 0.813
## asndrpf_shynss -0.074 0.014 -5.326 0.000
## bfi_neuro 0.014 0.011 1.270 0.204
## pvd_germ_avrsn 0.047 0.012 3.942 0.000
## pvd_infectblty 0.002 0.014 0.134 0.894
## spms_partner 0.013 0.013 0.972 0.331
## spms_self 0.139 0.017 8.069 0.000
## alternatives 0.103 0.015 6.874 0.000
## investment 0.009 0.013 0.675 0.500
## commitment -0.012 0.014 -0.851 0.395
## commnl_strngth -0.015 0.013 -1.161 0.246
## sxl_cmmnl_strn -0.001 0.013 -0.093 0.926
## ecr_avo -0.008 0.013 -0.660 0.509
## ecr_anx 0.004 0.003 1.570 0.116
## bfi_open ~~
## bfi_extra 0.113 0.020 5.652 0.000
## bfi_agree 0.022 0.012 1.926 0.054
## soi_r_attitude 0.047 0.022 2.163 0.031
## bfi_consc 0.033 0.012 2.794 0.005
## asndrpf_shynss -0.096 0.021 -4.540 0.000
## bfi_neuro -0.044 0.018 -2.417 0.016
## pvd_germ_avrsn -0.038 0.018 -2.118 0.034
## pvd_infectblty 0.020 0.023 0.858 0.391
## spms_partner 0.103 0.022 4.771 0.000
## spms_self 0.158 0.023 6.860 0.000
## alternatives 0.069 0.020 3.382 0.001
## investment -0.019 0.022 -0.869 0.385
## commitment -0.029 0.023 -1.276 0.202
## commnl_strngth 0.020 0.021 0.924 0.355
## sxl_cmmnl_strn -0.012 0.021 -0.570 0.569
## ecr_avo 0.002 0.020 0.095 0.924
## ecr_anx -0.004 0.004 -0.926 0.354
## bfi_extra ~~
## bfi_agree 0.088 0.014 6.398 0.000
## soi_r_attitude 0.108 0.022 4.814 0.000
## bfi_consc 0.065 0.013 5.091 0.000
## asndrpf_shynss -0.551 0.034 -16.395 0.000
## bfi_neuro -0.190 0.021 -9.029 0.000
## pvd_germ_avrsn -0.088 0.019 -4.635 0.000
## pvd_infectblty -0.053 0.023 -2.316 0.021
## spms_partner 0.066 0.021 3.089 0.002
## spms_self 0.184 0.023 7.841 0.000
## alternatives 0.074 0.021 3.577 0.000
## investment -0.049 0.022 -2.224 0.026
## commitment 0.021 0.023 0.914 0.361
## commnl_strngth 0.042 0.022 1.954 0.051
## sxl_cmmnl_strn 0.071 0.021 3.350 0.001
## ecr_avo -0.049 0.021 -2.375 0.018
## ecr_anx -0.006 0.004 -1.272 0.203
## bfi_agree ~~
## soi_r_attitude -0.025 0.013 -1.858 0.063
## bfi_consc 0.036 0.008 4.512 0.000
## asndrpf_shynss -0.106 0.015 -6.922 0.000
## bfi_neuro -0.122 0.015 -7.963 0.000
## pvd_germ_avrsn -0.095 0.014 -6.692 0.000
## pvd_infectblty -0.031 0.014 -2.184 0.029
## spms_partner -0.003 0.013 -0.268 0.789
## spms_self 0.011 0.013 0.818 0.413
## alternatives -0.021 0.012 -1.663 0.096
## investment 0.011 0.013 0.866 0.386
## commitment 0.026 0.014 1.875 0.061
## commnl_strngth 0.067 0.014 4.710 0.000
## sxl_cmmnl_strn 0.034 0.013 2.607 0.009
## ecr_avo -0.045 0.013 -3.426 0.001
## ecr_anx -0.004 0.003 -1.412 0.158
## soi_r_attitude ~~
## bfi_consc -0.057 0.014 -4.090 0.000
## asndrpf_shynss -0.136 0.024 -5.591 0.000
## bfi_neuro -0.050 0.021 -2.381 0.017
## pvd_germ_avrsn -0.115 0.022 -5.233 0.000
## pvd_infectblty -0.017 0.026 -0.662 0.508
## spms_partner 0.058 0.024 2.379 0.017
## spms_self 0.147 0.026 5.714 0.000
## alternatives 0.302 0.027 11.111 0.000
## investment -0.175 0.026 -6.641 0.000
## commitment -0.156 0.027 -5.824 0.000
## commnl_strngth -0.140 0.025 -5.492 0.000
## sxl_cmmnl_strn -0.082 0.024 -3.414 0.001
## ecr_avo 0.065 0.024 2.751 0.006
## ecr_anx 0.008 0.005 1.614 0.106
## bfi_consc ~~
## asndrpf_shynss -0.075 0.014 -5.417 0.000
## bfi_neuro -0.088 0.013 -6.583 0.000
## pvd_germ_avrsn 0.013 0.011 1.148 0.251
## pvd_infectblty -0.023 0.014 -1.692 0.091
## spms_partner 0.020 0.013 1.580 0.114
## spms_self 0.045 0.014 3.333 0.001
## alternatives -0.042 0.013 -3.316 0.001
## investment 0.016 0.013 1.185 0.236
## commitment 0.029 0.014 2.095 0.036
## commnl_strngth 0.052 0.014 3.808 0.000
## sxl_cmmnl_strn 0.035 0.013 2.729 0.006
## ecr_avo -0.033 0.013 -2.596 0.009
## ecr_anx -0.013 0.004 -3.336 0.001
## asendorpf_shyness ~~
## bfi_neuro 0.258 0.024 10.809 0.000
## pvd_germ_avrsn 0.079 0.020 3.893 0.000
## pvd_infectblty 0.082 0.025 3.283 0.001
## spms_partner -0.068 0.023 -2.943 0.003
## spms_self -0.232 0.026 -9.039 0.000
## alternatives -0.069 0.022 -3.138 0.002
## investment 0.074 0.024 3.105 0.002
## commitment -0.026 0.025 -1.055 0.291
## commnl_strngth -0.040 0.023 -1.691 0.091
## sxl_cmmnl_strn -0.081 0.023 -3.565 0.000
## ecr_avo 0.036 0.022 1.633 0.102
## ecr_anx 0.006 0.005 1.247 0.213
## bfi_neuro ~~
## pvd_germ_avrsn 0.115 0.019 5.997 0.000
## pvd_infectblty 0.202 0.024 8.376 0.000
## spms_partner -0.024 0.020 -1.205 0.228
## spms_self -0.106 0.022 -4.901 0.000
## alternatives -0.030 0.020 -1.553 0.120
## investment 0.097 0.022 4.468 0.000
## commitment -0.009 0.022 -0.389 0.697
## commnl_strngth -0.051 0.021 -2.444 0.015
## sxl_cmmnl_strn -0.031 0.020 -1.543 0.123
## ecr_avo 0.028 0.020 1.404 0.160
## ecr_anx 0.023 0.006 3.600 0.000
## pvd_germ_aversion ~~
## pvd_infectblty 0.091 0.022 4.055 0.000
## spms_partner 0.016 0.020 0.795 0.427
## spms_self 0.019 0.021 0.920 0.357
## alternatives -0.040 0.019 -2.066 0.039
## investment 0.039 0.021 1.887 0.059
## commitment 0.006 0.022 0.286 0.775
## commnl_strngth 0.003 0.020 0.165 0.869
## sxl_cmmnl_strn 0.006 0.020 0.329 0.742
## ecr_avo 0.042 0.020 2.157 0.031
## ecr_anx 0.008 0.004 1.791 0.073
## pvd_infectability ~~
## spms_partner -0.014 0.025 -0.550 0.582
## spms_self -0.025 0.026 -0.966 0.334
## alternatives -0.030 0.024 -1.222 0.222
## investment 0.049 0.026 1.882 0.060
## commitment 0.051 0.027 1.875 0.061
## commnl_strngth -0.009 0.026 -0.357 0.721
## sxl_cmmnl_strn -0.023 0.025 -0.920 0.358
## ecr_avo -0.019 0.025 -0.789 0.430
## ecr_anx 0.004 0.005 0.744 0.457
## spms_partner ~~
## spms_self 0.198 0.026 7.699 0.000
## alternatives -0.015 0.022 -0.651 0.515
## investment -0.012 0.024 -0.479 0.632
## commitment 0.059 0.025 2.336 0.019
## commnl_strngth 0.081 0.024 3.336 0.001
## sxl_cmmnl_strn 0.110 0.024 4.642 0.000
## ecr_avo -0.006 0.023 -0.249 0.803
## ecr_anx 0.008 0.005 1.579 0.114
## spms_self ~~
## alternatives 0.249 0.026 9.622 0.000
## investment -0.060 0.025 -2.405 0.016
## commitment -0.016 0.026 -0.634 0.526
## commnl_strngth 0.031 0.025 1.247 0.212
## sxl_cmmnl_strn 0.038 0.024 1.590 0.112
## ecr_avo -0.006 0.023 -0.272 0.786
## ecr_anx -0.004 0.005 -0.821 0.411
## alternatives ~~
## investment -0.149 0.024 -6.098 0.000
## commitment -0.366 0.029 -12.519 0.000
## commnl_strngth -0.234 0.026 -9.170 0.000
## sxl_cmmnl_strn -0.171 0.024 -7.189 0.000
## ecr_avo 0.237 0.025 9.579 0.000
## ecr_anx 0.017 0.006 2.954 0.003
## investment ~~
## commitment 0.329 0.030 10.889 0.000
## commnl_strngth 0.208 0.027 7.777 0.000
## sxl_cmmnl_strn 0.107 0.024 4.375 0.000
## ecr_avo -0.257 0.027 -9.557 0.000
## ecr_anx 0.005 0.005 1.081 0.280
## commitment ~~
## commnl_strngth 0.407 0.031 13.127 0.000
## sxl_cmmnl_strn 0.219 0.027 8.158 0.000
## ecr_avo -0.463 0.032 -14.587 0.000
## ecr_anx -0.020 0.007 -3.069 0.002
## communal_strength ~~
## sxl_cmmnl_strn 0.405 0.031 12.988 0.000
## ecr_avo -0.317 0.028 -11.482 0.000
## ecr_anx -0.009 0.005 -1.697 0.090
## sexual_communal_strength ~~
## ecr_avo -0.172 0.024 -7.154 0.000
## ecr_anx 0.002 0.005 0.434 0.665
## ecr_avo ~~
## ecr_anx 0.030 0.008 3.862 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .narq_5 0.805 0.037 21.506 0.000
## .narq_3 0.691 0.033 20.777 0.000
## .narq_15 0.703 0.034 20.866 0.000
## .narq_2 0.753 0.036 21.202 0.000
## .narq_11 0.944 0.043 22.153 0.000
## .narq_18 0.885 0.040 21.904 0.000
## .narq_16 0.619 0.031 20.176 0.000
## .narq_1 0.787 0.037 21.405 0.000
## .narq_14 0.943 0.043 22.149 0.000
## .narq_17 0.798 0.037 21.466 0.000
## .narq_4 0.734 0.035 21.080 0.000
## .narq_13 0.850 0.039 21.738 0.000
## .narq_6 0.773 0.036 21.323 0.000
## .narq_7 0.723 0.034 21.007 0.000
## .narq_12 0.751 0.035 21.188 0.000
## .narq_8 0.621 0.031 20.199 0.000
## .narq_9 0.762 0.036 21.259 0.000
## .narq_10 0.733 0.035 21.072 0.000
## .bfi_open_1 0.495 0.027 18.298 0.000
## .bfi_open_2 0.774 0.036 21.213 0.000
## .bfi_open_3 0.837 0.039 21.599 0.000
## .bfi_open_4 0.593 0.030 19.637 0.000
## .bfi_open_5 0.430 0.025 17.088 0.000
## .bfi_open_6 0.692 0.034 20.601 0.000
## .bfi_open_7r 0.911 0.041 21.978 0.000
## .bfi_open_8 0.599 0.030 19.704 0.000
## .bfi_open_9r 0.674 0.033 20.447 0.000
## .bfi_open_10 0.779 0.037 21.248 0.000
## .bfi_extra_1 0.454 0.022 20.321 0.000
## .bfi_extra_3 0.725 0.033 21.726 0.000
## .bfi_extra_2r 0.365 0.019 19.396 0.000
## .bfi_extra_4 0.666 0.031 21.518 0.000
## .bfi_extra_5r 0.403 0.020 19.842 0.000
## .bfi_extra_6 0.779 0.036 21.887 0.000
## .bfi_extra_7r 0.471 0.023 20.457 0.000
## .bfi_extra_8 0.299 0.016 18.327 0.000
## .bfi_agree_2 0.822 0.039 21.331 0.000
## .bfi_agree_3r 0.806 0.038 21.217 0.000
## .bfi_agree_1r 0.707 0.035 20.371 0.000
## .bfi_agree_4 0.807 0.038 21.223 0.000
## .bfi_agree_5 0.803 0.038 21.191 0.000
## .bfi_agree_6r 0.529 0.029 18.037 0.000
## .bfi_agree_7 0.792 0.038 21.112 0.000
## .bfi_agree_8r 0.425 0.027 15.815 0.000
## .bfi_agree_9 0.835 0.039 21.424 0.000
## .soi_r_atttd_6r 0.326 0.023 14.380 0.000
## .soi_r_attitd_4 0.376 0.024 16.005 0.000
## .soi_r_attitd_5 0.326 0.023 14.359 0.000
## .bfi_consc_2r 0.818 0.038 21.378 0.000
## .bfi_consc_3 0.600 0.031 19.400 0.000
## .bfi_consc_1 0.561 0.030 18.871 0.000
## .bfi_consc_9r 0.711 0.035 20.564 0.000
## .bfi_consc_4r 0.640 0.032 19.864 0.000
## .bfi_consc_5 0.610 0.031 19.521 0.000
## .bfi_consc_6 0.629 0.032 19.742 0.000
## .bfi_consc_7 0.660 0.033 20.079 0.000
## .bfi_consc_8r 0.676 0.033 20.238 0.000
## .asndrpf_shyn_4 0.380 0.020 19.403 0.000
## .asndrpf_shyn_2 0.456 0.023 20.225 0.000
## .asndrpf_shyn_5 0.683 0.032 21.547 0.000
## .asndrpf_shyn_3 0.340 0.018 18.794 0.000
## .asndrpf_shyn_1 0.339 0.018 18.777 0.000
## .bfi_neuro_2r 0.522 0.027 19.250 0.000
## .bfi_neuro_1 0.674 0.033 20.729 0.000
## .bfi_neuro_3 0.495 0.026 18.904 0.000
## .bfi_neuro_4 0.667 0.032 20.673 0.000
## .bfi_neuro_5r 0.525 0.027 19.292 0.000
## .bfi_neuro_8 0.644 0.031 20.491 0.000
## .bfi_neuro_6r 0.525 0.027 19.288 0.000
## .bfi_neuro_7 0.593 0.030 20.036 0.000
## .pvd_grm_vrsn_2 0.668 0.040 16.501 0.000
## .pvd_grm_vrs_3R 0.704 0.040 17.415 0.000
## .pvd_grm_vrsn_1 0.669 0.040 16.527 0.000
## .pvd_grm_vrs_4R 0.857 0.042 20.440 0.000
## .pvd_nfctblty_1 0.265 0.025 10.509 0.000
## .pvd_nfctblt_3R 0.419 0.026 16.164 0.000
## .pvd_nfctblty_2 0.412 0.026 15.948 0.000
## .spms_partner_1 0.377 0.027 14.169 0.000
## .spms_partner_2 0.263 0.027 9.814 0.000
## .spms_partnr_3R 0.522 0.029 18.299 0.000
## .spms_self_1 0.318 0.021 14.865 0.000
## .spms_self_2 0.239 0.020 11.676 0.000
## .spms_self_3R 0.436 0.024 18.098 0.000
## .alternatives_1 0.469 0.027 17.285 0.000
## .alternatives_2 0.587 0.031 19.250 0.000
## .alternatives_3 0.720 0.035 20.662 0.000
## .alternatives_4 0.680 0.033 20.291 0.000
## .alternatives_5 0.611 0.031 19.546 0.000
## .alternatives_6 0.788 0.037 21.188 0.000
## .investment_1 0.494 0.041 12.063 0.000
## .investment_2 0.630 0.038 16.368 0.000
## .investment_3 0.858 0.041 20.790 0.000
## .commitment_1 0.256 0.018 13.866 0.000
## .commitment_2 0.338 0.020 16.622 0.000
## .commitment_3 0.373 0.021 17.479 0.000
## .cmmnl_strngt_1 0.427 0.028 15.273 0.000
## .cmmnl_strng_2R 0.678 0.034 19.979 0.000
## .cmmnl_strngt_3 0.577 0.031 18.618 0.000
## .cmmnl_strngt_4 0.757 0.036 20.763 0.000
## .sxl_cmmnl_st_1 0.408 0.029 14.215 0.000
## .sxl_cmmnl_st_2 0.243 0.030 8.094 0.000
## .sxl_cmmnl_st_3 0.839 0.039 21.544 0.000
## .ecr_avo_1R 0.406 0.024 17.145 0.000
## .ecr_avo_2 0.868 0.040 21.839 0.000
## .ecr_avo_3R 0.379 0.023 16.504 0.000
## .ecr_avo_4 0.768 0.036 21.317 0.000
## .ecr_avo_5R 0.370 0.023 16.294 0.000
## .ecr_avo_6 0.856 0.039 21.783 0.000
## .ecr_anx_1 0.973 0.044 22.274 0.000
## .ecr_anx_2 0.337 0.026 12.992 0.000
## .ecr_anx_3 0.194 0.027 7.249 0.000
## .ecr_anx_4R 0.908 0.041 22.032 0.000
## .ecr_anx_5 0.961 0.043 22.234 0.000
## .ecr_anx_6 0.660 0.032 20.537 0.000
## narq 0.194 0.028 6.836 0.000
## bfi_open 0.504 0.042 12.055 0.000
## bfi_extra 0.545 0.041 13.339 0.000
## bfi_agree 0.177 0.028 6.348 0.000
## soi_r_attitude 0.673 0.046 14.728 0.000
## bfi_consc 0.181 0.028 6.473 0.000
## asndrpf_shynss 0.619 0.042 14.574 0.000
## bfi_neuro 0.477 0.041 11.782 0.000
## pvd_germ_avrsn 0.331 0.043 7.690 0.000
## pvd_infectblty 0.734 0.048 15.136 0.000
## spms_partner 0.622 0.046 13.455 0.000
## spms_self 0.681 0.045 15.046 0.000
## alternatives 0.530 0.043 12.321 0.000
## investment 0.505 0.052 9.722 0.000
## commitment 0.743 0.046 16.320 0.000
## commnl_strngth 0.572 0.045 12.634 0.000
## sxl_cmmnl_strn 0.591 0.046 12.728 0.000
## ecr_avo 0.593 0.044 13.619 0.000
## ecr_anx 0.026 0.011 2.404 0.016lcor_llm <- cfa(model,
sample.cov = cors, sample.nobs = 1000)## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative## Warning in lav_object_post_check(object): lavaan WARNING: covariance matrix of latent variables
## is not positive definite;
## use lavInspect(fit, "cov.lv") to investigate.rels_llm <- semTools::compRelSEM(lcor_llm)
summary(lcor)## lavaan 0.6.16 ended normally after 76 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 659.252
## Degrees of freedom 34
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## bfi_neuro =~
## bfi_neuro_2r 1.000
## bfi_neuro_1 3.887 1.049 3.704 0.000
## bfi_neuro_3 6.322 1.673 3.778 0.000
## bfi_neuro_4 5.136 1.368 3.756 0.000
## bfi_neuro_8 5.852 1.552 3.771 0.000
## bfi_neuro_6r 1.931 0.570 3.388 0.001
## bfi_neuro_7 5.938 1.574 3.773 0.000
## spms_self =~
## spms_self_1 1.000
## spms_self_2 0.720 0.112 6.422 0.000
## spms_self_3R 0.234 0.047 4.961 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## bfi_neuro ~~
## spms_self 0.009 0.005 1.771 0.077
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .bfi_neuro_2r 0.982 0.044 22.297 0.000
## .bfi_neuro_1 0.748 0.036 21.079 0.000
## .bfi_neuro_3 0.336 0.023 14.539 0.000
## .bfi_neuro_4 0.561 0.029 19.331 0.000
## .bfi_neuro_8 0.431 0.025 17.137 0.000
## .bfi_neuro_6r 0.937 0.042 22.110 0.000
## .bfi_neuro_7 0.414 0.025 16.749 0.000
## .spms_self_1 0.110 0.135 0.811 0.418
## .spms_self_2 0.538 0.074 7.251 0.000
## .spms_self_3R 0.950 0.043 22.017 0.000
## bfi_neuro 0.017 0.009 1.892 0.058
## spms_self 0.889 0.142 6.251 0.000rels <- data.frame(scale = names(rels_llm),
rel_llm = rels_llm,
rel_real = rels_real)
cor.test(rels_llm, rels_real)
Pearson's product-moment correlation
data: rels_llm and rels_real
t = 1.1522, df = 17, p-value = 0.2652
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.2108484 0.6445524
sample estimates:
cor
0.2691358
lm(rels_llm ~ rels_real)##
## Call:
## lm(formula = rels_llm ~ rels_real)
##
## Coefficients:
## (Intercept) rels_real
## 0.1909 0.5785ggplot(mapping = aes(x = rels_llm, y = rels_real, label = names(rels_llm))) +
geom_abline(linetype = "dashed") +
geom_point() +
ggrepel::geom_text_repel() +
coord_fixed(xlim = c(0,1), ylim = c(0,1)) ## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
estimated_rs <- standardizedsolution(lcor_real) %>%
left_join(standardizedsolution(lcor_llm), by = c("lhs", "op", "rhs")) %>%
select(lhs, op, rhs, est.std.x, est.std.y) %>%
left_join(rels %>% select(scale, rel_llm_lhs = rel_llm, rel_real_lhs = rel_real), by = c("lhs" = "scale")) %>%
left_join(rels %>% select(scale, rel_llm_rhs = rel_llm, rel_real_rhs = rel_real), by = c("rhs" = "scale"))
lv_rs <- estimated_rs %>% filter(op == "~~", !str_detect(lhs, "[0-9]"),
!str_detect(rhs, "[0-9]")) %>%
filter(lhs != rhs) %>%
mutate(est_manifest_llm = est.std.y * sqrt(rel_llm_lhs * rel_llm_rhs)) %>%
mutate(est_manifest_real = est.std.x * sqrt(rel_real_lhs * rel_real_rhs))
cor.test(lv_rs$est.std.x, lv_rs$est.std.y)
Pearson's product-moment correlation
data: lv_rs$est.std.x and lv_rs$est.std.y
t = 7.7117, df = 169, p-value = 1.019e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.3899780 0.6133057
sample estimates:
cor
0.5101915
cor.test(lv_rs$est_manifest_llm, lv_rs$est_manifest_real)
Pearson's product-moment correlation
data: lv_rs$est_manifest_llm and lv_rs$est_manifest_real
t = 8.3058, df = 169, p-value = 3.076e-14
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.4224623 0.6370034
sample estimates:
cor
0.5384002
lv_rs %>%
filter(lhs != "ecr_avo", rhs != "ecr_avo") %>%
{
cor.test(.$est.std.x, .$est.std.y)
}
Pearson's product-moment correlation
data: .$est.std.x and .$est.std.y
t = 7.58, df = 151, p-value = 3.247e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.3996145 0.6311052
sample estimates:
cor
0.5250022
cor.test(abs(lv_rs$est.std.x), abs(lv_rs$est.std.y))
Pearson's product-moment correlation
data: abs(lv_rs$est.std.x) and abs(lv_rs$est.std.y)
t = 5.9391, df = 169, p-value = 1.589e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.2831235 0.5324107
sample estimates:
cor
0.41554
qplot(lv_rs$est.std.y, lv_rs$est.std.x)## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
lm(est.std.y ~ est.std.x, lv_rs)##
## Call:
## lm(formula = est.std.y ~ est.std.x, data = lv_rs)
##
## Coefficients:
## (Intercept) est.std.x
## 0.08844 0.82108lm(est.std.x ~ est.std.y, lv_rs)##
## Call:
## lm(formula = est.std.x ~ est.std.y, data = lv_rs)
##
## Coefficients:
## (Intercept) est.std.y
## -0.02107 0.31701estimated_rs <- parameterestimates(lcor_real) %>%
left_join(parameterestimates(lcor_llm), by = c("lhs", "op", "rhs")) %>%
select(lhs, op, rhs, est.x, est.y)
lv_rs <- estimated_rs %>% filter(op == "~~", !str_detect(lhs, "[0-9]"),
!str_detect(rhs, "[0-9]")) %>%
filter(lhs != rhs)
cor.test(lv_rs$est.x, lv_rs$est.y)
Pearson's product-moment correlation
data: lv_rs$est.x and lv_rs$est.y
t = 7.3131, df = 169, p-value = 9.984e-12
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.3672414 0.5964764
sample estimates:
cor
0.4902924
cor.test(abs(lv_rs$est.x), abs(lv_rs$est.y))
Pearson's product-moment correlation
data: abs(lv_rs$est.x) and abs(lv_rs$est.y)
t = 6.0974, df = 169, p-value = 7.102e-09
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.2932581 0.5402837
sample estimates:
cor
0.424642
qplot(lv_rs$est.y, lv_rs$est.x)
lv_rs %>%
mutate(correlation = round(est.x, 2)) %>%
mutate(llm_based = round(est.y, 2)) %>%
mutate(scales = str_c(lhs, "\n", rhs)) %>%
ggplot(., aes(correlation, llm_based, label = scales)) +
geom_abline(linetype = "dashed") +
geom_point() +
coord_fixed(xlim = c(-1,1), ylim = c(-1,1)) -> p
ggplotly(p)